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Causal Attention for vision-Language Tasks

Causal Attention for Vision-Language Tasks

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Xu Zhang, Hanwang Qi, Guojun Cai, Jianfei Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Futurewei Technol Santa Clara CA USA Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9781665445092

We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. This effect causes harmful bias that misleads the attention module to focus on the spurious correlations in training data, damaging the model generalization. As the confounder is unobserved in general, we use the front-door adjustment to realize the causal intervention, which does not require any knowledge on the confounder. Specifically, CATT is implemented as a combination of 1) In-Sample Attention (IS-ATT) and 2) Cross-Sample Attention (CS-ATT), where the latter forcibly brings other samples into every IS-ATT, mimicking the causal intervention. CATT abides by the QK-V convention and hence can replace any attention module such as top-down attention and self-attention in Transformers. CATT improves various popular attention-based vision-language models by considerable margins. In particular, we show that CATT has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT [57], which uses fewer data and less computational power, comparable to the heavier UNITER [14]. Code is published in https://***/yangxuntu/lxmertcatt.

关键词： computer vision Correlation Codes Computational modeling Training data Transformers Data models

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Communication Efficient SGD via Gradient Sampling with Bayes Prior

Communication Efficient SGD via Gradient Sampling with Bayes...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Song, Liuyihan Zhao, Kang Pan, Pan Liu, Yu Zhang, Yingya Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Gradient compression has been widely adopted in data-parallel distributed training of deep neural networks to reduce communication overhead. Some literatures have demonstrated that large gradients are more important than small ones because they contain more information, such as Top-k compressor. Other mainstream methods, like random-k compressor and gradient quantization, usually treat all gradients equally. Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively. And we find taking both of them into consideration is the key to boost the final accuracy. So, we propose a novel gradient compressor: Gradient Sampling with Bayes Prior in this paper. Specifically, we sample important/large gradients based on the global gradient distribution, which is periodically updated across multiple workers. Then we introduce Bayes Prior into distribution model to further explore the gradients. We prove the convergence of our method for smooth non-convex problems in the distributed system. Compared with methods that running after high compression ratio at the expense of accuracy, we pursue no loss of accuracy and the actual acceleration benefit in practice. Experimental comparisons on a variety of computer vision tasks (e.g. image classification and object detection) and backbones (ResNet, MobileNetV2, InceptionV3 and AlexNet) show that our approach outperforms the state-of-the-art techniques in terms of both speed and accuracy, with the limitation of 100x compression ratio.

关键词： Training Degradation computer vision Image coding Quantization (signal) Object detection pattern recognition

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Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss

Event Camera Demosaicing via Swin Transformer and Pixel-focu...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yunfan Lu Yijie Xu Wenzong Ma Weiyu Guo Hui Xiong AI Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model’s applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://***/yunfanLu/ev-demosaic.

关键词： Training computer vision Computational modeling conferences computer architecture Cameras Transformers

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3D-MAN: 3D Multi-frame Attention Network for Object Detection

3D-MAN: 3D Multi-frame Attention Network for Object Detectio...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zetong Zhou, Yin Chen, Zhifeng Ngiam, Jiquan Chinese Univ Hong Kong Hong Kong Peoples R China Waymo LLC Mountain View CA USA Google Res Brain Team Mountain View CA USA

ISBN: (纸本)9781665445092

3D object detection is an important module in autonomous driving and robotics. However, many existing methods focus on using single frames to perform 3D detection, and do not fully utilize information from multiple frames. In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset. 3D-MAN first uses a novel fast single-frame detector to produce box proposals. The box proposals and their corresponding feature maps are then stored in a memory bank. We design a multi-view alignment and aggregation module, using attention networks, to extract and aggregate the temporal features stored in the memory bank. This effectively combines the features coming from different perspectives of the scene. We demonstrate the effectiveness of our approach on the large-scale complex Waymo Open Dataset, achieving state-of-the-art results compared to published single-frame and multi-frame methods.

关键词： computer vision Three-dimensional displays Fuses Aggregates Object detection Detectors Feature extraction

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Image-to-image Translation via Hierarchical Style Disentanglement

Image-to-image Translation via Hierarchical Style Disentangl...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Xinyang Zhang, Shengchuan Hu, Jie Cao, Liujuan Hong, Xiaopeng Mao, Xudong Huang, Feiyue Wu, Yongjian Ji, Rongrong Xiamen Univ Sch Informat Xiamen Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Tencent Youtu Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9781665445092

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. The code has been released at https://***/imlixinyang/HiSD.

关键词： Training computer vision Codes Scalability Process control Semisupervised learning pattern recognition

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ZeroScatter: Domain Transfer for Long Distance Imaging and vision through Scattering Media

ZeroScatter: Domain Transfer for Long Distance Imaging and V...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shi, Zheng Tseng, Ethan Bijelic, Mario Ritter, Werner Heide, Felix Princeton Univ Princeton NJ 08544 USA Mercedes Benz AG Stuttgart Germany Algolux Montreal PQ Canada

ISBN: (纸本)9781665445092

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method on in-the-wild captures, and the proposed method outperforms existing monocular descattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.

关键词： Training computer vision Rain Snow Scattering Training data Object detection

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Unsupervised Degradation Representation Learning for Blind Super-Resolution

Unsupervised Degradation Representation Learning for Blind S...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Longguang Wang, Yingqian Dong, Xiaoyu Xu, Qingyu Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Changsha Peoples R China Univ Tokyo Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (纸本)9781665445092

Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these methods suffer a severe performance drop when the real degradation is different from their assumption. To handle various unknown degradations in real-world applications, previous methods rely on degradation estimation to reconstruct the SR image. Nevertheless, degradation estimation methods are usually time-consuming and may lead to SR failure due to large estimation errors. In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation. Specifically, we learn abstract representations to distinguish various degradations in the representation space rather than explicit estimation in the pixel space. Moreover, we introduce a Degradation-Aware SR (DASR) network with flexible adaption to various degradations based on the learned representations. It is demonstrated that our degradation representation learning scheme can extract discriminative representations to obtain accurate degradation information. Experiments on both synthetic and real images show that our network achieves state-of-the-art performance for the blind SR task. Code is available at: https://***/LongguangWang/ DASR.

关键词： Degradation Estimation error computer vision Codes Superresolution Data mining Task analysis

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Scene-aware Generative Network for Human Motion Synthesis

Scene-aware Generative Network for Human Motion Synthesis

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Jingbo Yan, Sijie Dai, Bo Lin, Dahua Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We revisit human motion synthesis, a task useful in various real-world applications, in this paper. Whereas a number of methods have been developed previously for this task, they are often limited in two aspects: 1) focus on the poses while leaving the location movement behind, and 2) ignore the impact of the environment on the human motion. In this paper, we propose a new framework, with the interaction between the scene and the human motion taken into account. Considering the uncertainty of human motion, we formulate this task as a generative task, whose objective is to generate plausible human motion conditioned on both the scene and the human's initial position. This framework factorizes the distribution of human motions into a distribution of movement trajectories conditioned on scenes and that of body pose dynamics conditioned on both scenes and trajectories. We further derive a GAN-based learning approach, with discriminators to enforce the compatibility between the human motion and the contextual scene as well as the 3D- to-2D projection constraints. We assess the effectiveness of the proposed method on two challenging datasets, which cover both synthetic and real-world environments.

关键词： computer vision Uncertainty Semantics Dynamics Trajectory pattern recognition Task analysis

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Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection

Equalization Loss v2: A New Gradient Balance Approach for Lo...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Tan, Jingru Lu, Xin Zhang, Gang Yin, Changqing Li, Quanquan Tongji Univ Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently proposed decoupled training methods emerge as a dominant paradigm for long-tailed object detection. But they require an extra fine-tuning stage, and the disjointed optimization of representation and classifier might lead to suboptimal results. However, end-to-end training methods, like equalization loss (EQL), still perform worse than decoupled training methods. In this paper, we reveal the main issue in long-tailed object detection is the unbalanced gradients between positives and negatives, and find that EQL does not solve it well. To address the problem of Unbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that rebalances the training process for each category independently and equally. Extensive experiments are performed on the challenging LVIS benchmark. EQI, v2 outperforms origin EQI, by about 4 points overall AP with 14 similar to 18 points improvements on the rare categories. More importantly, it also surpasses decoupled training methods. Without further tuning for the Open Images dataset, EQL v2 improves EQL by 7.3 points AP showing strong generalization ability. Codes have been released at https://***/tztztztztz/eq1v2

关键词： Training computer vision Codes Object detection Benchmark testing Boosting pattern recognition

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Open-Vocabulary Object Detection Using Captions

Open-Vocabulary Object Detection Using Captions

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zareian, Alireza Dela Rosa, Kevin Hu, Derek Hao Chang, Shih-Fu Snap Inc Seattle WA 98121 USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.

关键词： Location awareness Training Deep learning computer vision Costs Annotations Object detection

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